The present invention relates to cross-domain medical image analysis and cross-domain synthesis of medical images, and more particularly, to cross-domain medical image analysis and cross-domain medical image synthesis using deep learning networks.
A multitude of imaging modalities, such as computed tomography (CT), diffuser tensor imaging (DTI), T1-weighted magnetic resonance imaging (MRI), T2-weighted MRI, ultrasound, X-ray, positron emission tomography (PET), etc., can be used for medical image analysis of a of a patient. Each of these imaging modalities captures different characteristics of the underlying anatomy and the relationship between any two modalities is highly nonlinear. These different imaging techniques provide physicians with varied tools and information for making accurate diagnoses. However, sensor heterogeneity creates challenges for developing effective automatic image analysis platforms. In particular, algorithms that work well on one modality can be rendered useless on data collected from a different type of scanner.
In many practical medical image analysis problems, a situation is often encountered in which medical image data available for training, for example for machine learning based anatomical object detection, has a different distribution or representation than the medical image data given during testing due to modality heterogeneity or domain variation. Due to variations in the image characteristics across modalities, medical image analyses algorithms trained with data from one modality may not work well when applied to medical image data from a different modality. One way to address this issue is to collect large amounts of training data from each imaging modality. However, this solution is impractical since collecting medical images is often time consuming and expensive.
Cross-modal synthesis generates medical images in a desired target modality from given source modality images. The ability to synthesize medical images without actual acquisition has many potential applications, such as atlas construction, virtual enhancement, multi-modal registration, and segmentation. Various approaches for cross-modal synthesis have been proposed, but such approaches are typically tailored to specific applications or based on various heuristics.